Discrimination of apnea type by medical device

ABSTRACT

Disclosed herein, among other things, are methods and apparatus related to identification of apnea type. One aspect of the present subject matter provides a method for real-time apnea discrimination. The method includes sensing an impedance-based tidal volume signal to monitor a respiratory cycle of a patient, and detecting a reduction in tidal swing using the sensed impendence to detect an apnea event. When the apnea event is detected, a shape of the sensed signal is compared to a stored signal shape to determine whether the apnea event is primarily an obstructive sleep apnea (OSA) event or primarily a central sleep apnea (CSA) event, in various embodiments.

CLAIM OF PRIORITY

This application is a continuation of U.S. application Ser. No.15/213,120, filed Jul. 18, 2016, which is a continuation of U.S.application Ser. No. 14/638,142, filed Mar. 4, 2015, now issued as U.S.Pat. No. 9,402,563, which claims the benefit of priority under 35 U.S.C.§ 119(e) of U.S. Provisional Patent Application Ser. No. 61/975,084,filed on Apr. 4, 2014, which is herein incorporated by reference in itsentirety.

CROSS REFERENCE TO RELATED APPLICATION

This application is related to co-pending, commonly assigned, U.S.Patent Application Ser. No. 61/975,090, entitled “METHODS AND APPARATUSFOR APNEA THERAPY STIMULATION”, filed on Apr. 4, 2014, which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

This application relates generally to medical devices and, moreparticularly, to systems, devices and methods related to discriminationof apnea type.

BACKGROUND

Respiratory diseases include disorders that affect breathing duringsleep. Examples of respiratory disorders include central sleep apnea(CSA) and obstructive sleep apnea (OSA). Sleep apnea refers to thecessation of breathing during sleep. CSA is associated with incorrectsensing of carbon dioxide or oxygen levels in the blood. If nervereceptors do not send the correct neural signals, in essence deceivingthe brain by reporting incorrect levels of carbon dioxide or oxygen, anincidence of CSA can occur. OSA is associated with an obstruction of theupper airway. Both CSA and OSA have serious health consequences,including association with cardiac arrhythmias and worsening heartfailure. CSA and OSA can occur separately or together in a given patientduring the night.

Typically, therapy for CSA is not effective for OSA, and therapy for OSAis not effective for CSA. Therefore, there is a need in the art forreal-time apnea discrimination.

SUMMARY

Disclosed herein, among other things, are methods and apparatus relatedto identification of apnea type. One aspect of the present subjectmatter provides a method for real-time apnea discrimination. The methodmay include sensing an impedance-based tidal volume signal to monitor arespiratory cycle of a patient, and detecting a reduction in tidal swingusing the sensed impendence to detect an apnea event. When the apneaevent is detected, a shape of the sensed signal is compared to a storedsignal shape to determine whether the apnea event is primarily anobstructive sleep apnea (OSA) event or primarily a central sleep apnea(CSA) event, in various embodiments.

One aspect of the present subject matter provides a medical device forapnea discrimination. The device may include a sensor configured tosense an impedance-based tidal volume signal to monitor a respiratorycycle of a patient and a processor adapted to be connected to thesensor. The processor may be configured to detect a reduction in tidalswing using the sensed impendence to detect an apnea event and tocompare a shape of the sensed signal to a stored signal shape todetermine whether the detected apnea event is primarily an obstructivesleep apnea (OSA) event or primarily a central sleep apnea (CSA) event,in various embodiments.

One aspect of the present subject matter provides a medical device forapnea discrimination. The device may include a sensor configured tosense a parameter related to heart sounds of a patient and a processoradapted to be connected to the sensor. The processor may be configuredto use the sensed parameter to determine whether a detected apnea eventis primarily an obstructive sleep apnea (OSA) event or primarily acentral sleep apnea (CSA) event.

This Summary is an overview of some of the teachings of the presentapplication and not intended to be an exclusive or exhaustive treatmentof the present subject matter. Further details about the present subjectmatter are found in the detailed description and appended claims. Thescope of the present invention is defined by the appended claims andtheir equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are illustrated by way of example in the figures ofthe accompanying drawings. Such embodiments are demonstrative and notintended to be exhaustive or exclusive embodiments of the presentsubject matter.

FIG. 1 illustrates an example of a therapy system with a programmer.

FIG. 2 illustrates an example of a system that includes an implantablemedical device (IMD), such as the system of FIG. 1.

FIG. 3 illustrates an example of an external device, such as theprogrammer of FIG. 1.

FIG. 4 illustrates a flow diagram for an example of a method ofidentifying apnea, according to various embodiments of the presentsubject matter.

FIGS. 5A-5C illustrate graphical diagrams showing examples of signalsthat may be used to discriminate OSA and CSA, according to variousembodiments of the present subject matter.

FIGS. 6A-6B illustrate flow diagrams for an example of methods ofadjusting an apnea discrimination threshold, according to variousembodiments of the present subject matter.

FIG. 7 illustrates a flow diagram for an example of a method ofidentifying apnea, according to various embodiments of the presentsubject matter.

DETAILED DESCRIPTION

The following detailed description refers to subject matter in theaccompanying drawings which show, by way of illustration, specificaspects and embodiments in which the present subject matter may bepracticed. These embodiments are described in sufficient detail toenable those skilled in the art to practice the present subject matter.References to “an,” “one,” or “various” embodiments in this disclosureare not necessarily to the same embodiment, and such referencescontemplate more than one embodiment. The following detailed descriptionis, therefore, not to be taken in a limiting sense, and the scope isdefined only by the appended claims, along with the full scope of legalequivalents to which such claims are entitled.

Obstructive sleep apnea (OSA) and central sleep apnea (CSA) are drivenby different causes and hence target therapies are different for eachtype of sleep apnea. Continuous positive airway pressure (CPAP) workswell for OSA but is generally not effective for treating CSA.Diaphragmatic stimulation can be used to treat CSA but is generally noteffective for OSA, since respiratory drive already exists during OSA. Inaddition, patients may not experience OSA or CSA episodes exclusively,as typically they may experience both OSA and CSA episodes throughoutthe night. Presently, the classification (or discrimination) of apatient into OSA or CSA patient is largely based on predominantprevalence of the apnea type during the night. However, even patientsthat experience largely one type of apnea and are classified as thatapnea type, do experience occasional apneic episodes that are of theother type. In some cases, it may be valuable to not apply OSA therapyfor sparse CSA episodes in OSA patients or vice versa. There is achallenge to quickly identify what type of apnea event is in progress,so proper treatment can be applied in a timely manner. Therefore, thereis a need in the art for real-time apnea discrimination, so that theproper therapy can be delivered for the episode.

Various examples provided herein use an algorithm to characterize apneaand learn likelihoods of observing the types of apnea specific to anindividual patient. The system may use that probability analysis toassist in determining type of apnea, in various embodiments. Disclosedherein, among other things, are methods and apparatus related toidentification of apnea type. One aspect of the present subject matterprovides a method for real-time or near real-time apnea discrimination.These terms indicate that, although there may be some processing delays,the apnea discrimination is able to process the apneic events as theyoccur without an observable delay (e.g. real time) or with observabledelays that are insignificant for processing the apneic events as theyoccur (near real time). The method may include sensing animpedance-based tidal volume signal to monitor a respiratory cycle of apatient, and detecting a reduction in tidal swing using the sensedimpendence to detect an apnea event. When the apnea event is detected, ashape of the sensed signal is compared to a stored signal shape todetermine whether the apnea event is primarily an OSA event or primarilya CSA event, in various embodiments. This discrimination is done in realtime based on features from early portions of the apnea event, so thatthe proper therapy can be provided in a timely manner to assist intreating the event.

Disclosed herein, among other things, are methods and apparatus relatedto identification of apnea type. One aspect of the present subjectmatter provides a method for real-time apnea discrimination. The methodmay include sensing an impedance-based tidal volume signal to monitor arespiratory cycle of a patient, and detecting a reduction in tidal swingusing the sensed impendence to detect an apnea event. When the apneaevent is detected, a shape of the sensed signal is compared to a storedsignal shape to determine whether the apnea event is primarily anobstructive sleep apnea (OSA) event or primarily a central sleep apnea(CSA) event, in various embodiments.

According to various embodiments, the stored signal shape may include anormal tidal swing shape for the patient, a stored prior OSA waveformtemplate, a sine wave (as a surrogate for CSA template), and/or a squarewave (as a surrogate for OSA template). If the shape of the sensedsignal is similar to the normal tidal swing shape, the apnea event isdetermined to be primarily a CSA event in various embodiments. Invarious embodiments, if the shape of the sensed signal is not similar tothe normal tidal swing shape, the apnea event is determined to beprimarily an OSA event. There are various methods or algorithms that maybe implemented to analyze the similarity between the signals. Forexample, a statistical analysis may be performed on the signals todetermine a calculated similarity value, and this similarity value maybe compared to a similarity threshold to make a determination that thesignals are similar. Various embodiments further include calculatinglikelihood for each apnea event based on historical prevalence of CSAevents and OSA events for the patient. Some embodiments include usingthe calculated likelihood to determine whether the apnea event isprimarily an obstructive sleep apnea (OSA) event or primarily a centralsleep apnea (CSA) event. Therapy is provided to the patient based on thedetermination, in various embodiments.

One aspect of the present subject matter provides a medical device forapnea discrimination. The device may include a sensor configured tosense an impedance-based tidal volume signal to monitor a respiratorycycle of a patient and a processor adapted to be connected to thesensor. The processor may be configured to detect a reduction in tidalswing using the sensed impendence to detect an apnea event and tocompare a shape of the sensed signal to a stored signal shape todetermine whether the detected apnea event is primarily an obstructivesleep apnea (OSA) event or primarily a central sleep apnea (CSA) event,in various embodiments.

One aspect of the present subject matter provides a medical device forapnea discrimination. The device may include a sensor configured tosense a parameter related to heart sounds of a patient and a processoradapted to be connected to the sensor. The processor may be configuredto use the sensed parameter to determine whether a detected apnea eventis primarily an obstructive sleep apnea (OSA) event or primarily acentral sleep apnea (CSA) event. Additionally or alternatively, theprocessor may be configured to use the sensed parameter to determinewhether the patient may be classified as primarily experiencing OSA oras primarily experiencing CSA. In addition to determining the patient'sprimary apnea type (OSA or CSA), or as a separate determination, theprocessor may be configured to use the sensed parameter to determine amix or ratio of OSA and CSA for that individual patent. The mix or ratiomay be displayed or otherwise communicated to a clinician for diagnosticassistance. In some cases, the display may present the mix or ratio ofOSA and CSA in textual format, as a trend, or in any other suitablemanner.

According to various embodiments, the processor may be configured to usevariability in low frequency components of a spectrum of at least one ofR-R intervals, S1 amplitude, S2 amplitude, S3 amplitude or systolic timeintervals over time to determine prevalence of primarily OSA events andprimarily CSA events for the patient. In various embodiments, examplesof systolic time intervals include: pre-ejection period (PEP): Q-S1,R-S1, ejection time, diastolic interval and S1-S2 interval. Variousembodiments include any of these timing intervals or an entire R-Rinterval.

The processor may be configured to conduct a time-frequencydecomposition of the signal of interest using any standard timefrequency methods, such as short term Fourier transform, wavelettransform, Wigner-Ville transform, etc. to obtain a spectrogram of thesignal over some period of time which could range from the last fewminutes, to the last few hours, to the last few days. The processor maybe configured to use the variability along the frequency axis on thespectrogram to determine a likelihood of observing CSA or OSA on thenext apnea event, in various embodiments. In various embodiments, theprocessor may be configured to adjust a discrimination threshold for thenext apnea event based on the determined likelihood. The processor maybe configured to track a set of consistent spectral peaks on thespectrogram and use energies of tracked peaks to determine relativeprevalence of primarily OSA events and primarily CSA events for thepatient, in an embodiment. Dynamic programming algorithms such as theViterbi algorithm may be employed to track the set of most consistentpeaks over time. Such algorithms usually define a cost-function based onthe energies of candidate peaks and the offsets (in frequencies) betweenpeaks across time steps, and use minimization of the cost function toderive a set of consistent peaks across time. For example, a consistentpeak track could be defined as a collection of peaks over time (one peakat every time step on the spectrogram) that maximizes the sum ofenergies at those peaks and minimizes the absolute sum of thedifferences in frequencies between peaks at successive time steps. Invarious embodiments, the processor may be configured to use energy ofthe tracked peaks relative to total spectral energy to determine alikelihood of observing CSA or OSA on the next apnea event. Theprocessor may be configured to adjust a discrimination threshold for thenext apnea event based on the determined likelihood, according tovarious embodiments. For example, if the total energy at the tracked setof peaks is 80% of the total spectral energy over the last N hours, thenthe a priori likelihood of observing a CSA event at the next apneicepisode is 80%. Thus, the next apneic event is declared to be an OSAevent only if similarity of the early portions of the tidal volumesignal to an OSA template is at least 4 (80/20) times the similarity toa CSA template. In this way, the detection for the current event isbiased towards being labeled a CSA given that higher likelihood ofobserving a CSA based on the spectrogram analysis.

Some embodiments may include a memory to store historical data of CSAevents and OSA events for the patient, and the processor may beconfigured to calculate likelihood for each apnea event based on thehistorical data. In various embodiments, the processor may be configuredto use the calculated likelihood to determine whether the next apneaevent is likely to be primarily an obstructive sleep apnea (OSA) eventor primarily a central sleep apnea (CSA) event. For example, if apatient has historically experiences 80% CSA episodes and 20% OSAepisodes over the last N hours or last N days, then the a priorilikelihood of observing a CSA event at the next apneic episode is 80%.

In some embodiments, a confidence score may be assigned to the apneicclassification over a period (N hours, for example) based upon thecomparison of the stored historical data of CSA and OSA events for thatpatient over the period with a spectrogram-based assessment ofprevalence of CSA and OSA over the same period. If there is a closematch between the classification results (stored as historical data) andthe spectrogram based estimates, the results are labeled as highconfidence. If there is a mismatch between the assigned classificationand spectrogram based assessment results are flagged as low confidence.

FIG. 4 illustrates a flow diagram for an example of a method ofidentifying apnea, according to various embodiments of the presentsubject matter. At 402, a patient's respiratory cycle is monitored, suchas with minute ventilation (MV) using Z-based tidal volume in anembodiment. Other parameters indicative of a patient's respiratory cyclemay be monitored without departing from the scope of the present subjectmatter. If a reduction in tidal swing is detected at 404, an apnea eventis detected and a shape of the reduced tidal swing signal is compared toa normal tidal swing at 406. If the shape is similar to normal tidalswing, the apnea event is primarily a CSA event at 408. If the shape isnot similar to normal tidal swing, the apnea event is primarily an OSAevent at 410. Various embodiments compare shape similarity to a storedprior OSA template. Further embodiments compare shape similarity toideal sine waves as a surrogate for a CSA waveform template. Stillfurther embodiments compare shape similarity to ideal square waves as asurrogate for an OSA waveform template.

In various embodiments, the present subject matter determines apnea anddiscriminates apnea type in a first portion of an apnea event, so thatappropriate therapy can be applied in a timely manner. In one embodimentapnea is discriminated in the first 10 to 15 percent of an apneaepisode. Other time periods can be used without departing from the scopeof the present subject matter. In various embodiments, an a priorilikelihood is developed from a prevalence of episodes determined fromhistorical data, and the likelihood is used to discriminate currentapnea events. According to various embodiments, shape of the monitoredimpedance characteristic is used to discriminate apnea events. Forexample, OSA changes the shape by flattening the characteristic. In oneexample, CSA has the same shape with a reduced magnitude.

According to various embodiments, the present subject matter may usepatency from an impedance-based tidal volume signal to discriminate OSAand CSA. Various embodiments may use a priori likelihoods for an apneaepisode based upon the historic prevalence of the two types of apneas orlong term (robust) analysis of historic data in a given patient, forapnea discrimination. The present subject matter may use an estimate ofprevalence of CSA and OSA to assess the likelihood of the next apneaevent, in an embodiment. Quantification of prevalence of CSA and OSA maybe determined as discussed, including examining variability in Fouriertransforms, in an embodiment

FIGS. 5A-5C illustrate graphical diagrams showing examples of signalsthat may be used to discriminate OSA and CSA, according to variousembodiments of the present subject matter. The depicted graphs showshort term Fourier transforms of R-R intervals computed over shortepochs at different points in time throughout the night, in variousembodiments. In some embodiments, S1 amplitude, S2 amplitude, S3amplitude or any systolic time intervals may be used as the signal ofinterest instead of R-R intervals. CSA episodes are usually associatedwith oscillations at a characteristic system frequency (which is belowthe respiration frequency of 0.1-0.3 Hz) resulting in Cheney stokespattern. Cheney stokes oscillations lead to a waxing and waning ofrespiratory drive at the same characteristic frequency which in turnsets up oscillations at the Cheney stokes frequency in the intrathoracicpressures. Such an oscillatory pattern leads to oscillations in Heartrate, S1 amplitude, S2 amplitude, S3 amplitudes and time intervals(systolic and diastolic) that show up as a narrow band of spectral peaksat the characteristic Cheney stokes frequency on the spectrogram. On theother hand, in case of OSA episodes there are no oscillations at anycharacteristic frequency and hence, the spectral components in the lowfrequency region of the spectrogram are completely random. Theseexpected profiles of CSA and OSA in the low frequency region of thespectrogram can be used to quantify the prevalence of CSA and OSAepisodes over a given period. In FIG. 5A, variability in low frequency(below respiration) components of R-R intervals is very high and isindicative of a primarily OSA apnea event in an embodiment. For example,various embodiments find that it is more likely that OSA dominates anevent when there are greater amount of variability in the peaks. FIG. 5Bshows an embodiment of a primarily CSA apnea event, as there is lessvariability observed in the peaks of the low frequency components. FIG.5C illustrates an embodiment of a more complex profile, that includesboth CSA and OSA. A set of peaks could be isolated over time that areclosely located in frequency. Such a set would be characterized by lowvariation in frequency (due to constraint on the peaks being closelylocated in frequency) and thus would be characteristic of CSA portionsof apneic episodes. The relative energies of peaks within this setrelative to total spectral energy in the spectrogram could be indicativeof the portion of apneic episodes within this time period that are CSA.In various embodiments, the spectral profiles depicted in FIGS. 5A-5Cand their analysis may be used to set an a priori likelihood for thenext apnea event for a patient.

In some embodiment any variable out of R-R interval, S1 amplitude, S2amplitude, S3 amplitude, systolic or diastolic interval may be used asthe signal of interest to perform the spectral analysis. In anotherembodiment, spectral analysis can be individually conducted on more thanone of the variables and the results combined/averaged for a more robustestimation.

FIGS. 6A-6B illustrate flow diagrams for an example of methods ofdetermining a priori likelihood of next apneic event based on theprofile of the low frequency portion of the spectrogram over the last Nminutes, or last N hours or last N days by adjusting an apneadiscrimination threshold, according to various embodiments of thepresent subject matter. FIG. 6A illustrates an example of a method usinglow frequency spectrogram variability to adjust an apnea discriminationthreshold, in an embodiment. At 602, low frequency variability of animpedance-based tidal volume signal spectrogram is monitored over aprogrammable time period (N min or N days or N hours). Observedvariability is projected on a continuum between an ideal low level(indicative of CSA) and an ideal high level (indicative of OSA), at 604.At 606, the projection is used as a likelihood of observing CSA or OSAfor the next apnea event. At 608, the discrimination threshold isadjusted for the next apnea event based on the likelihood, in variousembodiments. For example, if spectrogram analysis indicates that theprevalence of CSA was 80% over the last N hours, then the a priorilikelihood of observing the next apneic event as CSA is 80% andthreshold is set such that similarity of the next event to an OSAtemplate should be at least four times the similarity to a CSA templatein order for it to be labeled OSA. In this way, the classification ofthe next event is biased towards being labeled CSA in light of the higha priori likelihood of it being a CSA based on spectrogram analysis.

FIG. 6B illustrates an example of a method using alignment of peaks on aspectrogram to adjust an apnea discrimination threshold, in anembodiment. At 612, a set of closely aligned peaks on a spectrogram aretracked in time. Dynamic programming algorithms such as the Viterbialgorithm may be employed to track the set of most consistent over time.Such algorithms usually define a cost-function based on the energies ofcandidate peaks and the offsets (in frequencies) between peaks acrosstime steps and use minimization of the cost function to derive a set ofconsistent peaks across time. For example, a consistent peak track couldbe defined as a collection of peaks over time (one peak at every timestep on the spectrogram) that maximizes the sum of energies at thosepeaks and minimizes the absolute sum of the differences in frequenciesbetween peaks at successive time steps. At 614, tracked spectral peaksare categorized as having a characteristic frequency (CSA) and otherpeaks are categorized as OSA. Energy of tracked peaks versus other peaksis used as a likelihood of observing CSA or OSA, at 616. At 618, thediscrimination threshold is adjusted for the next apnea event based onthe likelihood, in various embodiments.

FIG. 7 illustrates a flow diagram for an example of a method ofidentifying apnea, according to various embodiments of the presentsubject matter. The depicted method embodiment includes three parts:likelihood estimation 702, real-time discrimination 712 and storage 722.At 704, likelihood estimation includes gathering physiologic data over aprogrammable time period. Analysis is performed on the gathered data tocharacterize apnea events for a patient over a CSA-OSA spectrum, at 706.At 708, a probability and/or prediction function is generated for inputinto a CSA or OSA determination. The process is updated at 710. Theprocess can be updated programmably, periodically, intermittently and/orcontinually, in various embodiments.

At 714, real-time discrimination includes monitoring at least one of:respiratory cycle with MV, PPT, ECG, S1/S3 heart sounds and cardiacintervals. If CSA is determined at 716, an apnea-hypopnea index isupdated at 724. In various embodiments, an apnea index includes CSA andOSA counts, durations, etc. If no CSA is determined at 716, then adetermination is made of OSA at 718. If OSA is determined at 718, anapnea-hypopnea index is updated at 724. If no OSA is determined at 716,then an apnea-hypopnea index is updated at 724. Additional implantedsensors may be used to further augment CSA/OSA classification, such asspectral analysis of S1/S3 heart sounds (amplitudes), pulse transit time(ECG/cervical impedance plethysmography), ECG-based spectral analysis,and cardiac interbeat interval time series, in various embodiments. Onceapnea is determined and discriminated, the appropriate therapy can beapplied in a closed loop system, such as the apnea therapy inco-pending, commonly assigned, U.S. Patent Application Ser. No.61/975,090, entitled “METHODS AND APPARATUS FOR APNEA THERAPYSTIMULATION”, filed on Apr. 4, 2014, which is hereby incorporated byreference in its entirety.

The present subject matter provides a diagnostic tool for clinicians todetermine extent of apnea (e.g. apnea-hypopnea index (AHI)), todetermine type of apnea (OSA, CSA, or where the patient falls on thecentral-obstructive apnea spectrum), and/or to provide clinicianguidance on determining that patient's therapy (CPAP type/settings;phrenic or hypoglossal stimulation; etc.). This diagnostic tool can beprovided in any medical device including, for example, an implantablestimulator, (e.g. pacemaker, implantable cardioverter defibrillator(ICD), subcutaneous ICD (S-ICD), cardiac resynchronization therapy (CRT)device, or nerve stimulator such as a vagal nerve stimulator, a carotidsinus stimulator, a hypoglossal nerve stimulator, or a phrenic nervestimulator), an implantable diagnostic device or monitor, a wearabledevice (e.g. patches or vests), or external medical devices.

FIG. 1 illustrates an example of stimulation system 100. The system mayinclude an external medical device, such as external patch basedsensors, in various embodiments. Other types of external medical devicesmay be used for apnea discrimination without departing from the scope ofthe present subject matter. Various embodiments of the system mayinclude an implantable medical device (IMD) 102 implanted into apatient's tissue and an external device such as a programmer 104external to the patient's body. The programmer 104 and the IMD 102 maycommunicate via a telemetry link 106. Embodiments of the system withoutsensing are included within the scope of the present subject matter.

FIG. 2 illustrates an example of a system that includes an implantablemedical device (IMD) and an external device such as the programmer 104of FIG. 1. The IMD 102 may be coupled to at least a portion of astimulation lead 202 having one or more electrodes 204 disposed on thelead 202. The leads 202 may include leads or electrodes that are in oron the heart, on vagus, hypoglossal, or phrenic nerves, on the carotidsinus, and may include subcutaneous electrodes (such as S-ICD orsubcutaneous HF monitor), or other locations on or in the patient'sbody. In various embodiments, the lead 202 may include cuffelectrode(s), helical electrode(s), or other electrode configurationconfigured to deliver monopolar, bipolar or multipolar stimulation. Thelead 202 may have dimensions suitable to place the one or moreelectrodes 204 proximate to a site of a neural pathway. For example, theelectrode(s) may be intravascular electrodes or may be configured tootherwise be placed proximate to a nerve. For example, electrode(s) maybe placed in the internal jugular vein (IJV) to stimulate a cervicalvagus nerve, or may be placed in the carotid sheath at a site proximatethe vagus nerve of a patient. Monopolar delivery occurs when a selectedelectrode is activated along with a reference electrode amongst theelectrodes 204, so that electrical energy is transmitted between theselected electrode and the reference electrode. Monopolar delivery mayalso occur when one or more of the selected electrodes are activatedalong with a large group of electrodes located from the electrode(s) 204so as to create a monopolar effect; that is, electrical energy isconveyed from the selected electrode(s) 204 in a relatively isotropicmanner. Bipolar delivery occurs when two of the electrodes 204 areactivated as anode and cathode, so that electrical energy is transmittedbetween the activated electrodes. Multipolar delivery occurs whenmultiple electrodes 204 are activated.

The IMD 102 may include a stimulation circuitry 206, sensor circuitry210, a controller circuitry 212, a transceiver/telemetry circuitry 214,and a memory 216. The stimulation circuitry 206 is electrically coupledto the electrodes 204 using conductors of the stimulation lead 202. Thestimulation circuitry 206 delivers electrical signals to the electrodes204 to stimulate the desired target to provide stimulation. Theprogrammer 104 may be used to program stimulation parameters into thememory 216. The controller circuitry 212 may use the programmedstimulation parameter to control the stimulator circuitry 206 togenerate the stimulation that corresponds to the programmed stimulationparameters.

The IMD 102 may include one or more sensor(s) 208 for sensingphysiological parameters such as cardiac contractions which may be usedto determine heart rate (beats per minute or bpm) or rhythm information,tissue impedance (ohms), intrinsic atrial-ventricular (AV) delay(seconds), heart sounds, respiratory sounds, pressure, respiration,acceleration (activity and posture), nerve traffic, chemical parameters,or the like. The sensor(s) 208 can be located external to the IMD 102housing, or encapsulated within the IMD 102 housing. The sensor(s) 208can be attached to the sensor circuitry 210. The sensor circuitry 210can include various components, such as instrumentation amplifiers,signal filters, etc., that process the electrical signals fordetermining the physiological parameters.

The sensor circuitry 210 feeds the physiological parameters to thecontroller circuitry 212. The controller circuitry 212 controls variousoperations of the IMD 102 and can include programmable microprocessors,microcontrollers, or the like. For example, the controller circuitry 212may be programmed to perform therapy and send control signals to theneural stimulator circuitry 206 for transmitting electrical stimulationpulses to the electrodes 204. The controller circuitry 212 analyzes thedetermined physiological parameters and other parameters inputted by theuser using the programmer 104 to assess appropriate therapy regime andsend control signals to neural stimulation circuitry 206 fortransmitting stimulation pulses to the patient's target tissue.

The transceiver/telemetry circuitry 214 may communicate the determinedphysiological parameters to the programmer 104 located external to thepatient's body. The telemetry circuitry 214 may use a suitablecommunication protocol, such as, the medical implant communicationservice (MICS) in the bandwidth of 402-405 MHz, for communicating withthe programmer 104.

The memory 216 may be used to store the stimulation parameters receivedfrom programmer 104 and the physiological parameters determined by thesensor circuitry 210. For example, the memory 216 can store at least oneyear of daily lead impedance measurements and/or program usage. Inanother example, the memory can store lifetime energy use data for thedevice. In yet another example, the memory may store a list ofmeasurements over time of a sensed parameter, for example, heart rate,for assessing the appropriate therapy regime.

The IMD 102 can be encased in a biocompatible metallic, polymeric, orcomposite housing (not shown), according to various embodiments. Thehousing protects the components of the IMD 102 from coming in contactwith the patient's tissue. Additionally, the IMD 102 includes a powersource such as a battery for delivering power to the IMD 102.

FIG. 3 illustrates an example of an external device such as theprogrammer 104 of FIG. 1. The programmer 104 may be a portable device orhand held device that includes a controller/processor 302, a memory 304,a display 306, an input/output (I/O) unit 308, a transceiver/telemetryunit 310, and a communications interface 312. The programmer 104 may behoused within a polymeric, metallic or composite housing.

The controller 302 controls various operations of the programmer 104.The controller 302 may include any suitable computing device, forexample, microprocessors, microcomputers, microcontrollers, digitalsignal processors, central processing units, state machines, logiccircuitries, and/or any devices that manipulate signals based onoperational instructions. Among other capabilities, the controller 302may be configured to fetch and execute computer-readable instructionsstored in the memory 304. Further, the controller 302 may be configuredwith standard or customized operating systems, such as, MicrosoftWindows, Linux, UNIX, or the like, with one or more custom softwareinstalled to control the operations of other components of theprogrammer 104. The controller 302 may be a fixed or portable computingdevice such as a desktop computer or a laptop, tablet or phone. Thetelemetry unit 310 communicates with the IMD 102 using the telemetrylink 106 (FIG. 1). In some embodiments, the telemetry unit 310 allowsthe programmer 104 to control and program the IMD 102. In addition, thetelemetry unit 310 allows the programmer 104 to communicate with the IMD102 (shown in FIG. 2).

VARIOUS EXAMPLES

An example (e.g. “Example 1”) of a medical device for apneadiscrimination may include a sensor configured to sense animpedance-based tidal volume signal to monitor a respiratory cycle of apatient, and a processor adapted to be connected to the sensor. Theprocessor may be configured to detect a reduction in tidal swing usingthe sensed impendence to detect an apnea event and to compare a shape ofthe sensed signal to a stored signal shape to determine whether thedetected apnea event is primarily an obstructive sleep apnea (OSA) eventor primarily a central sleep apnea (CSA) event.

In Example 2, the subject matter of Example 1 may optionally beconfigured such that the stored signal shape includes a normal tidalswing shape for the patient.

In Example 3, the subject matter of Example 2 may optionally beconfigured such that, if the shape of the sensed signal is similar tothe normal tidal swing shape, the apnea event is determined to beprimarily a CSA event.

In Example 4, the subject matter of any one or any combination ofExamples 2-3 may optionally be configured such that, if the shape of thesensed signal is not similar to the normal tidal swing shape, the apneaevent is determined to be primarily an OSA event.

In Example 5, the subject matter of any one or any combination ofExamples 1-4 may optionally be configured such that the stored signalshape includes a stored prior OSA waveform template.

In Example 6, the subject matter of any one or any combination ofExamples 1-5 may optionally be configured such that the stored signalshape includes a sine wave or a square wave.

In Example 7, the subject matter of any one or any combination ofExamples 1-6 may optionally be configured such that the processor isconfigured to use variability in low frequency components of a spectrumof at least one of R-R intervals, S1 amplitude, S2 amplitude, S3amplitude or systolic time intervals to determine prevalence ofprimarily OSA events and primarily CSA events for the patient.

In Example 8, the subject matter of Example 7 may optionally beconfigured such that the processor is configured to use the variabilityto determine a likelihood of observing CSA or OSA on the next apneaevent.

In Example 9, the subject matter of Example 8 may optionally beconfigured such that the processor is configured to adjust adiscrimination threshold for the next apnea event based on thedetermined likelihood.

In Example 10, the subject matter of any one or any combination ofExamples 1-6 may optionally be configured such that the processor isconfigured to track a set of spectral peaks on a spectrogram and useenergies of tracked peaks to determine relative prevalence of primarilyOSA events and primarily CSA events for the patient.

In Example 11, the subject matter of Example 10 may optionally beconfigured such that the processor is configured to use energy of thetracked peaks relative to total spectral energy to determine alikelihood of observing CSA or OSA on the next apnea event.

In Example 12, the subject matter of Example 11 may optionally beconfigured such that the processor is configured to adjust adiscrimination threshold for the next apnea event based on thedetermined likelihood.

In Example 13, the subject matter of any one or any combination ofExamples 1-12 may optionally be configured such that the device furtherincludes a memory to store historical data of CSA events and OSA eventsfor the patient.

In Example 14, the subject matter of Example 13 may optionally beconfigured such that the processor is configured to calculate alikelihood for each apnea event based on the historical data.

In Example 15, the subject matter of Example 14 may optionally beconfigured such that the processor is configured to use the calculatedlikelihood to determine whether the apnea event is primarily anobstructive sleep apnea (OSA) event or primarily a central sleep apnea(CSA) event.

An example (e.g. “Example 16”) of a method for apnea discrimination mayinclude sensing an impedance-based tidal volume signal to monitor arespiratory cycle of a patient, detecting a reduction in tidal swingusing the sensed impendence to detect an apnea event, and, when theapnea event is detected, comparing a shape of the sensed signal to astored signal shape to determine whether the apnea event is primarily anobstructive sleep apnea (OSA) event or primarily a central sleep apnea(CSA) event.

In Example 17, the subject matter of Example 16 may optionally beconfigured such that the stored signal shape includes a normal tidalswing shape for the patient.

In Example 18, the subject matter of Example 16 may optionally beconfigured such that the stored signal shape includes a stored prior OSAwaveform template.

In Example 19, the subject matter of Example 16 may optionally beconfigured such that the stored signal shape includes a sine wave.

In Example 20, the subject matter of Example 16 may optionally beconfigured such that the stored signal shape includes a square wave.

In Example 21, the subject matter of Example 17 may optionally beconfigured such that, if the shape of the sensed signal is similar tothe normal tidal swing shape, the apnea event is determined to beprimarily a CSA event.

In Example 22, the subject matter of Example 17 may optionally beconfigured such that, if the shape of the sensed signal is not similarto the normal tidal swing shape, the apnea event is determined to beprimarily an OSA event.

In Example 23, the subject matter of Example 16 may optionally beconfigured such that the method further includes calculating likelihoodfor each apnea event based on historical prevalence of CSA events andOSA events for the patient.

In Example 24, the subject matter of Example 23 may optionally beconfigured such that the method further includes using the calculatedlikelihood to determine whether the apnea event is primarily anobstructive sleep apnea (OSA) event or primarily a central sleep apnea(CSA) event.

In Example 25, the subject matter of Example 16 may optionally beconfigured such that the method further includes assigning a confidencescore to the determination of whether the apnea event is primarily anobstructive sleep apnea (OSA) event or primarily a central sleep apnea(CSA) event.

An example (e.g. “Example 26”) of a medical device for apneadiscrimination may include a sensor configured to sense animpedance-based tidal volume signal to monitor a respiratory cycle of apatient, and a processor adapted to be connected to the sensor. Theprocessor may be configured to detect a reduction in tidal swing usingthe sensed impendence to detect an apnea event and to compare a shape ofthe sensed signal to a stored signal shape to determine whether thedetected apnea event is primarily an obstructive sleep apnea (OSA) eventor primarily a central sleep apnea (CSA) event.

In Example 27, the subject matter of Example 26 may optionally beconfigured such that the processor is configured to use variability inlow frequency components of a spectrum of at least one of R-R intervals,S1 amplitude, S2 amplitude, S3 amplitude or systolic time intervals todetermine prevalence of primarily OSA events and primarily CSA eventsfor the patient.

In Example 28, the subject matter of Example 27 may optionally beconfigured such that the processor is configured to use the variabilityto determine a likelihood of observing CSA or OSA on the next apneaevent.

In Example 29, the subject matter of Example 28 may optionally beconfigured such that the processor is configured to adjust adiscrimination threshold for the next apnea event based on thedetermined likelihood.

In Example 30, the subject matter of Example 26 may optionally beconfigured such that the processor is configured to track a set ofspectral peaks on a spectrogram and use energies of tracked peaks todetermine relative prevalence of primarily OSA events and primarily CSAevents for the patient.

In Example 31, the subject matter of Example 30 may optionally beconfigured such that the processor is configured to use energy of thetracked peaks relative to total spectral energy to determine alikelihood of observing CSA or OSA on the next apnea event, and whereinthe processor is configured to adjust a discrimination threshold for thenext apnea event based on the determined likelihood.

An example (e.g. “Example 32”) of a medical device for apneadiscrimination may include a sensor configured to sense a parameterrelated to heart sounds of a patient, and a processor adapted to beconnected to the sensor. The processor may be configured to use thesensed parameter to determine whether a detected apnea event isprimarily an obstructive sleep apnea (OSA) event or primarily a centralsleep apnea (CSA) event.

In Example 33, the subject matter of Example 32 may optionally beconfigured such that the parameter related to heart sounds of thepatient includes variability in low frequency components of a spectrumof at least one of R-R intervals, S1 amplitude, S2 amplitude, S3amplitude or systolic time intervals.

In Example 34, the subject matter of Example 32 may optionally beconfigured such that the device further includes a memory to storehistorical data of CSA events and OSA events for the patient.

In Example 35, the subject matter of Example 34 may optionally beconfigured such that the processor is configured to calculate likelihoodfor each apnea event based on the historical data and to use thecalculated likelihood to determine whether the apnea event is primarilyan obstructive sleep apnea (OSA) event or primarily a central sleepapnea (CSA) event.

One of ordinary skill in the art will understand that, the modules andother circuitry shown and described herein can be implemented usingsoftware, hardware, and/or firmware. Various disclosed methods may beimplemented as a set of instructions contained on a computer-accessiblemedium capable of directing a processor to perform the respectivemethod.

This application is intended to cover adaptations or variations of thepresent subject matter. It is to be understood that the abovedescription is intended to be illustrative, and not restrictive. Forexample, the present subject matter can be applied to other medicalprocedures where heating or ablation of tissue is desired. The scope ofthe present subject matter should be determined with reference to theappended claims, along with the full scope of legal equivalents to whichsuch claims are entitled.

What is claimed is:
 1. A system, comprising: a sensor configured tosense an impedance-based tidal volume signal to monitor a respiratorycycle of a patient; and an external system including a device configuredto communicate with the sensor, the external system including aprocessor configured to detect a reduction in tidal swing using thesensed impendence to detect an apnea event and to determine whether thedetected apnea event is primarily an obstructive sleep apnea (OSA) eventor primarily a central sleep apnea (CSA) event.
 2. The system of claim1, wherein the external system is configured to compare a stored signalshape to the signal to determine whether the detected apnea event isprimarily an OSA event or primarily a CSA event.
 3. The system of claim2, wherein the stored signal shape includes a normal tidal swing for thepatient.
 4. The system of claim 2, wherein the stored signal shapeincludes a stored prior OSA waveform template.
 5. The system of claim 2,wherein the stored signal shape includes an ideal sine wave as asurrogate for a CSA waveform template.
 6. The system of claim 1, whereinthe device includes a diagnostic device.
 7. The system of claim 1,wherein the device includes a stimulator.
 8. The system of claim 1,wherein the device includes a wearable device.
 9. The system of claim 8,wherein the wearable device includes a patch.
 10. The system of claim 8,wherein the wearable device includes a vest.
 11. A method for apneadiscrimination, the method comprising: sensing an impedance-based tidalvolume signal using a sensor to monitor a respiratory cycle of apatient; and using an external system to communicate with the sensor andto process the sensed parameter to detect a reduction in tidal swingusing the sensed impendence to detect an apnea event and to determinewhether the detected apnea event is primarily an obstructive sleep apnea(OSA) event or primarily a central sleep apnea (CSA) event.
 12. Themethod of claim 11, comprising using a processor in an implantablestimulator in communication with the sensor to process the sensedparameter.
 13. The method of claim 11, comprising using a processor in anerve stimulator in communication with the sensor to process the sensedparameter.
 14. The method of claim 11, comprising using a processor inan implantable diagnostic device in communication with the sensor toprocess the sensed parameter.
 15. The method of claim 11, comprisingusing a processor in a wearable device in communication with the sensorto process the sensed parameter.
 16. The method of claim 11, comprisingusing a processor in an external medical device in communication withthe sensor to process the sensed parameter.
 17. The method of claim 11,further comprising using the processor to determine a mix or ratio ofOSA and CSA for the patient.
 18. The method of claim 17, furthercomprising using a display connected to the processor to present the mixor ratio of OSA and CSA to a clinician for diagnostic assistance. 19.The method of claim 11, further comprising using a memory connected tothe processor to store historical data of CSA events and OSA events forthe patient.
 20. The method of claim 19, further comprising using theprocessor to calculate likelihood for each apnea event based on thehistorical data and to use the calculated likelihood to determinewhether the apnea event is primarily an OSA event or primarily a centralsleep apnea CSA event.